{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.         -0.70710678 -1.38873015  0.52489066  0.59299945 -1.35873244]\n",
      " [ 0.         -0.70710678  0.46291005  0.87481777  0.81537425  1.01904933]\n",
      " [ 0.          1.41421356  0.9258201  -1.39970842 -1.4083737   0.33968311]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "X = np.array([\n",
    "    [0., 0., 5., 13., 9., 1.],\n",
    "    [0., 0., 13., 15., 10., 15.],\n",
    "    [0., 3., 15., 2., 0., 11.]\n",
    "])\n",
    "print(preprocessing.scale(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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 "nbformat": 4,
 "nbformat_minor": 2
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